In recent years, the use of photovoltaic power generation systems both in the United States and abroad has progressively grown largely due to a continually increasing demand for renewable energy resources. This growth has been fed by advances in manufacturing of photovoltaic systems that have dramatically decreased the cost per watt of electricity generated. Government incentives have also further decreased per-watt costs. Photovoltaic systems are widely usable for standalone off-grid power, supplemental electricity sources and as power grid-connected systems. When integrated into a power grid, photovoltaic systems are collectively operated as a fleet of individual photovoltaic power generation plants, which may be deployed at different physical locations within a geographic region.
A modern electrical grid is a power generation, transmission and distribution infrastructure that delivers electrical power from suppliers to consumers often across a large geographically disbursed area. Power generation and consumption must be constantly balanced across an entire power grid, as electricity is consumed almost immediately upon production. Power failures within a power grid are of grave concern. A power failure in one part of a power grid could potentially cause electrical current to reroute from remaining power generators over transmission lines of insufficient capacity, thereby overloading transmission lines and short circuiting transformers with cascading power failures and widespread outages. As a result, both planners and operators must precisely determine real-time power generation and consumption throughout a power grid. They must also be able to accurately forecast power production from all sources, including photovoltaic systems, to meet expected power grid-wide demand.
Accurate power production data is particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid's overall energy mix. At the individual photovoltaic plant level, power production forecasting first involves obtaining a prediction of solar irradiance derived from ground-based measurements, satellite imagery or numerical weather prediction models. The predicted solar irradiance is then combined with photovoltaic simulation models, which generates a forecast of individual plant power production. The individual forecasts can also combined into a photovoltaic power generation fleet forecast, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; and 8,165,813, all issued to Hoff on Apr. 12, 2012, the disclosures of which are incorporated by reference, for use in power grid planning and operations.
Inaccuracies in a photovoltaic power generation forecast reduce the forecast's value to power grid planners and operators by discounting the degree to which they can rely on photovoltaic power to meet short term needs and by undermining their overall confidence in photovoltaic power as a reliable power source when compared to other power sources. Actual measured photovoltaic fleet power output can be compared to the simulated power output as predicted in a power forecast to help identify the magnitude of inaccuracy. Discrepancies between measured and simulated photovoltaic fleet power output are generally attributable to errors in the forecasted solar irradiance, the simulation models that combine photovoltaic system specifications with weather data to forecast individual photovoltaic plant production, or the models that combine the results from many photovoltaic plants to develop a photovoltaic fleet forecast.
Satellite imagery and numerical weather prediction models are commonly used to forecast the solar irradiance values that are provided as inputs into the photovoltaic simulation models and which are then determine forecast accuracy. Conventionally, simulation inaccuracies are addressed by dividing the photovoltaic power production forecasting into two, separate and unconnected processes. The first process produces the best possible weather data, with a particular emphasis on the best possible solar radiation data. The second process, which is treated independently of the first process, develops the most accurate photovoltaic simulation model possible; however, the simulation model assumes perfect weather data inputs.
This approach presents two shortcomings. First, the approach fails to recognize that perfect weather data is unavailable. There will always be inaccuracies in weather data, whether the result of calibration or other errors, such as in the case of ground-based data collection devices, or incorrect model translation, such as with solar irradiance data derived from satellite imagery or numerical weather prediction models. Second, the correct model calibration of a photovoltaic simulation model is dependent upon both the characteristics of the physical photovoltaic plant whose power production is being forecast and the irradiance data source and its inaccuracies. Tuning a photovoltaic plant to the most accurate weather data available and then using a different weather data source to actually forecast power production is suboptimal. The two processes need to be integrated into a unified photovoltaic forecasting methodology.
Therefore, a need remains for an approach to improving forecasts of the power output of a photovoltaic plant.